The artificial neural network mimics the behaviour of clusters of brain cells. Using a neural network involves training the system to build an audio profile of each music genre based on its beat and tempo.
According to the researchers, Mao-Yuan Kao and Chang-Biau Yang of National Sun Yat-sen University Shyue-Horng Shiau of the Chang Jung Christian University, most music fans can put a tune into a particular genre even on a first listen. However, for archivists and others with large collections of unclassified music, an automated approach that assigns the main genre to each tune would save a lot of time and effort.
The team has so far tested their approach on a collection of several hundred ballroom dance music files, such as the cha-cha-cha, jive, quickstep, and tango. Although this approach was not 100 per cent accurate, it has outperformed the existing – Ellis and Dixon – methods.
However, the team suggest that the neural network will need further training to be able to classify more diverse music collections such as classical, jazz, pop and so on.
The research is published this month in the International Journal of Intelligent Information and Database Systems.
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Date Published: August 27, 2009
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